# SampleFix: Learning to Generate Functionally Diverse Fixes

arXiv: https://arxiv.org/abs/1906.10502 · HTML: https://mlsanity.com/paper/1906.10502/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [SimSCOOD: Systematic Analysis of Out-of-Distribution Generalization in Fine-tuned Source Code Models](https://mlsanity.com/paper/2210.04802/) (2022)

## Abstract (arXiv)

Automatic program repair holds the potential of dramatically improving the productivity of programmers during the software development process and correctness of software in general. Recent advances in machine learning, deep learning, and NLP have rekindled the hope to eventually fully automate the process of repairing programs. However, previous approaches that aim to predict a single fix are prone to fail due to uncertainty about the true intend of the programmer. Therefore, we propose a generative model that learns a distribution over potential fixes. Our model is formulated as a deep conditional variational autoencoder that can efficiently sample fixes for a given erroneous program. In order to ensure diverse solutions, we propose a novel regularizer that encourages diversity over a semantic embedding space. Our evaluations on common programming errors show for the first time the generation of diverse fixes and strong improvements over the state-of-the-art approaches by fixing up to 45% of the erroneous programs. We additionally show that for the 65% of the repaired programs, our approach was able to generate multiple programs with diverse functionalities.